




'''simple docstring'''







import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class 	_snake_case:






 def __init__(self       :  Optional[Any]    ,		a       :  List[Any]=2    ,		a       :  str=3    ,		a       :  Any=64    ,		a       :  str=None    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     np.random.default_rng(a    )
    A__       =     length
    A__       =     rng.normal(size=(length,)    ).astype(np.floataa    )
    A__       =     a * self.x + b + rng.normal(scale=0.1    ,		size=(length,)    ).astype(np.floataa    )






 def __len__(self       :  List[Any]    )       ->   Optional[Any]:






    """simple docstring"""


    return self.length






 def __getitem__(self       :  Tuple    ,		a       :  Optional[Any]    )       ->   int:






    """simple docstring"""


    return {"x": self.x[i], "y": self.y[i]}




class 	_snake_case(			torch.nn.Module      ):






 def __init__(self       :  List[str]    ,		a       :  Dict=0    ,		a       :  int=0    ,		a       :  int=False    )       ->   Union[str, Any]:






    """simple docstring"""


    super().__init__()
    A__       =     torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
    A__       =     torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
    A__       =     True






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  Any=None    )       ->   Optional[int]:






    """simple docstring"""


    if self.first_batch:
       print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
       A__       =     False
    return x * self.a[0] + self.b[0]




class 	_snake_case(			torch.nn.Module      ):






 def __init__(self       :  List[str]    ,		a       :  int=0    ,		a       :  int=0    ,		a       :  Tuple=False    )       ->   Tuple:






    """simple docstring"""


    super().__init__()
    A__       =     torch.nn.Parameter(torch.tensor(a    ).float()    )
    A__       =     torch.nn.Parameter(torch.tensor(a    ).float()    )
    A__       =     True






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Union[str, Any]=None    )       ->   Union[str, Any]:






    """simple docstring"""


    if self.first_batch:
       print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
       A__       =     False
    return x * self.a + self.b







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase = 16						):






   '''simple docstring'''







   from datasets import load_dataset
   from transformers import AutoTokenizer

   A__       =     AutoTokenizer.from_pretrained('bert-base-cased'						)
   A__       =     {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
   A__       =     load_dataset('csv'      ,data_files=UpperCAmelCase						)
   A__       =     datasets['train'].unique('label'						)

   A__       =     {v: i for i, v in enumerate(UpperCAmelCase						)}

   def tokenize_function(UpperCAmelCase						):
      # max_length=None => use the model max length (it's actually the default)
      A__       =     tokenizer(
          examples['sentence1']      ,examples['sentence2']      ,truncation=UpperCAmelCase      ,max_length=UpperCAmelCase      ,padding='max_length'						)
      if "label" in examples:
         A__       =     [label_to_id[l] for l in examples['label']]
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   A__       =     datasets.map(
       UpperCAmelCase      ,batched=UpperCAmelCase      ,remove_columns=['sentence1', 'sentence2', 'label']      ,)

   def collate_fn(UpperCAmelCase						):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(UpperCAmelCase      ,padding='max_length'      ,max_length=128      ,return_tensors='pt'						)
      return tokenizer.pad(UpperCAmelCase      ,padding='longest'      ,return_tensors='pt'						)

   # Instantiate dataloaders.
   A__       =     DataLoader(tokenized_datasets['train']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=2						)
   A__       =     DataLoader(tokenized_datasets['validation']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=1						)

   return train_dataloader, eval_dataloader








'''simple docstring'''







import re
import string

import numpy as np

import datasets


lowerCAmelCase_     						=				'''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

lowerCAmelCase_     						=				'''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

lowerCAmelCase_     						=				'''
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       , _KWARGS_DESCRIPTION      )
class 	_snake_case(			datasets.Metric      ):






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Dict:






    """simple docstring"""


    return datasets.MetricInfo(
        description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(
            {
                'predictions': datasets.Value('string'    ,		id='sequence'    ),
                'references': datasets.Value('string'    ,		id='sequence'    ),
            }    )    ,		reference_urls=[]    ,		)






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  str    ,		a       :  Tuple    ,		a       :  Tuple=None    ,		a       :  Dict=False    ,		a       :  Dict=False    ,		a       :  List[str]=False    ,		)       ->   int:






    """simple docstring"""


    if regexes_to_ignore is not None:
       for s in regexes_to_ignore:
          A__       =     np.array([re.sub(a    ,		''    ,		a    ) for x in predictions]    )
          A__       =     np.array([re.sub(a    ,		''    ,		a    ) for x in references]    )
    else:
       A__       =     np.asarray(a    )
       A__       =     np.asarray(a    )

    if ignore_case:
       A__       =     np.char.lower(a    )
       A__       =     np.char.lower(a    )

    if ignore_punctuation:
       A__       =     string.punctuation.maketrans(''    ,		''    ,		string.punctuation    )
       A__       =     np.char.translate(a    ,		table=a    )
       A__       =     np.char.translate(a    ,		table=a    )

    if ignore_numbers:
       A__       =     string.digits.maketrans(''    ,		''    ,		string.digits    )
       A__       =     np.char.translate(a    ,		table=a    )
       A__       =     np.char.translate(a    ,		table=a    )

    A__       =     predictions == references

    return {"exact_match": np.mean(a    ) * 1_00}


